This model is a fine-tuned version of nvidia/mit-b0 on the Tani04/mars_testbed_terrain dataset. It achieves the following results on the evaluation set:

Loss: 5.5421
Mean Iou: 0.3524
Mean Accuracy: 0.4470
Overall Accuracy: 0.6053
Accuracy Flat Bedrock: 0.0
Accuracy Flat Gravel: 0.8431
Accuracy Hard Gravel: 0.1552
Accuracy Obstacle: 0.7895
Iou Flat Bedrock: 0.0
Iou Flat Gravel: 0.7604
Iou Hard Gravel: 0.1121
Iou Obstacle: 0.5371

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 6e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 60

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Flat Gravel	Accuracy Hard Gravel	Accuracy Obstacle	Iou Flat Gravel	Iou Hard Gravel	Iou Obstacle
0.9524	2.5	20	2.7762	0.2467	0.4256	0.5007	0.9625	0.1759	0.5642	0.4426	0.1131	0.4309
1.9458	5.0	40	3.0021	0.3148	0.4469	0.5622	0.9322	0.1733	0.6820	0.6568	0.1131	0.4893
0.5475	7.5	60	3.3026	0.3227	0.4611	0.5852	0.9585	0.1652	0.7207	0.6585	0.1147	0.5174
0.3696	10.0	80	3.7134	0.3445	0.4580	0.6020	0.9118	0.1511	0.7693	0.7352	0.1082	0.5347
0.3826	12.5	100	4.1692	0.3456	0.4555	0.5962	0.9079	0.1575	0.7565	0.7465	0.1107	0.5252
0.2651	15.0	120	4.1228	0.3453	0.4423	0.5933	0.8489	0.1522	0.7683	0.7503	0.1071	0.5237
0.3288	17.5	140	4.2263	0.3515	0.4577	0.6159	0.8787	0.1492	0.8030	0.7429	0.1106	0.5527
0.1629	20.0	160	4.6687	0.3453	0.4441	0.5827	0.8782	0.1597	0.7385	0.7653	0.1086	0.5073
0.377	22.5	180	4.5768	0.3475	0.4440	0.5953	0.8478	0.1601	0.7680	0.7527	0.1124	0.5250
0.3951	25.0	200	4.6531	0.3393	0.4327	0.5730	0.8427	0.1562	0.7317	0.7553	0.1053	0.4965
0.1941	27.5	220	4.5228	0.3530	0.4543	0.6116	0.8698	0.1508	0.7966	0.7556	0.1104	0.5462
0.237	30.0	240	4.5266	0.3526	0.4541	0.6031	0.8839	0.1580	0.7744	0.7650	0.1128	0.5325
0.2397	32.5	260	5.0232	0.3486	0.4460	0.5950	0.8608	0.1578	0.7656	0.7606	0.1111	0.5229
0.1909	35.0	280	5.4829	0.3564	0.4563	0.6200	0.8635	0.1462	0.8155	0.7600	0.1102	0.5554
0.1391	37.5	300	5.3365	0.3553	0.4581	0.6153	0.8811	0.1515	0.8000	0.7610	0.1123	0.5481
0.2368	40.0	320	5.4860	0.3527	0.4525	0.6051	0.8730	0.1551	0.7820	0.7635	0.1116	0.5357
0.1269	42.5	340	5.1693	0.3508	0.4462	0.5977	0.8546	0.1590	0.7712	0.7646	0.1124	0.5263
0.0917	45.0	360	5.3999	0.3552	0.4541	0.6138	0.8633	0.1517	0.8015	0.7618	0.1119	0.5471
2.1454	47.5	380	6.0907	0.3528	0.4518	0.6062	0.8685	0.1520	0.7867	0.7640	0.1102	0.5371
0.1348	50.0	400	5.6524	0.3541	0.4487	0.6087	0.8456	0.1535	0.7958	0.7633	0.1119	0.5411
0.1323	52.5	420	5.7016	0.3532	0.4492	0.6062	0.8531	0.1549	0.7888	0.7632	0.1120	0.5376
0.1165	55.0	440	5.6639	0.3502	0.4419	0.6033	0.8218	0.1545	0.7913	0.7538	0.1115	0.5355
0.1574	57.5	460	5.8704	0.3532	0.4474	0.6085	0.8378	0.1554	0.7963	0.7585	0.1132	0.5411
0.0974	60.0	480	5.5421	0.3524	0.4470	0.6053	0.8431	0.1552	0.7895	0.7604	0.1121	0.5371

Framework versions

Transformers 4.46.3
Pytorch 2.4.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3

Tani04/segformer-b0-mars-testbed-fewshot

segformer-b0-mars-testbed-fewshot

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Tani04/
segformer-b0-mars-testbed-fewshot